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# mmdet to groundingdino
import argparse
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
# convert the functions from mmdet to groundingdino
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, in_channel // 4, 4).transpose(1, 2)
x = x[:, [0, 2, 1, 3], :]
x = x.reshape(out_channel, in_channel)
return x
def correct_unfold_norm_order(x):
in_channel = x.shape[0]
x = x.reshape(in_channel // 4, 4).transpose(0, 1)
x = x[[0, 2, 1, 3], :]
x = x.reshape(in_channel)
return x
def convert(ckpt):
"""Inverse mapping of checkpoint parameters to their original names."""
# Create a dictionary to hold the reversed checkpoint
new_ckpt = OrderedDict()
for k, v in list(ckpt.items()):
new_v = v # Start with the original value
# Inverse rules based on the convert function (from specific to general)
if k.startswith('decoder'):
new_k = k.replace('decoder', 'module.transformer.decoder')
if 'norms.2' in new_k:
new_k = new_k.replace('norms.2', 'norm1')
if 'norms.1' in new_k:
new_k = new_k.replace('norms.1', 'catext_norm')
if 'norms.0' in new_k:
new_k = new_k.replace('norms.0', 'norm2')
if 'norms.3' in new_k:
new_k = new_k.replace('norms.3', 'norm3')
if 'cross_attn_text' in new_k:
new_k = new_k.replace('cross_attn_text', 'ca_text')
new_k = new_k.replace('attn.in_proj_weight', 'in_proj_weight')
new_k = new_k.replace('attn.in_proj_bias', 'in_proj_bias')
new_k = new_k.replace('attn.out_proj.weight', 'out_proj.weight')
new_k = new_k.replace('attn.out_proj.bias', 'out_proj.bias')
if 'ffn.layers.0.0' in new_k:
new_k = new_k.replace('ffn.layers.0.0', 'linear1')
if 'ffn.layers.1' in new_k:
new_k = new_k.replace('ffn.layers.1', 'linear2')
if 'self_attn.attn' in new_k:
new_k = new_k.replace('self_attn.attn', 'self_attn')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
# encoder部分最后的reg_layer_id是6，和decoder区分开来
elif k.startswith('bbox_head.reg_branches.6'):
if k.startswith('bbox_head.reg_branches.6.0'):
new_k = k.replace('bbox_head.reg_branches.6.0',
'module.transformer.enc_out_bbox_embed.layers.0')
if k.startswith('bbox_head.reg_branches.6.2'):
new_k = k.replace('bbox_head.reg_branches.6.2',
'module.transformer.enc_out_bbox_embed.layers.1')
if k.startswith('bbox_head.reg_branches.6.4'):
new_k = k.replace('bbox_head.reg_branches.6.4',
'module.transformer.enc_out_bbox_embed.layers.2')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('query_embedding'):
new_k = k.replace('query_embedding', 'module.transformer.tgt_embed')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('bbox_head.reg_branches'):
# mmdet直接省略了参数名的一部分，需要查看groundingdino的checkpoint
# groundingdino有两部分参数值是一致的
# 分别是module.bbox_embed和module.transformer.decoder.embed
# 所以mmdet直接将两部分参数进行了“合并”
reg_layer_id = int(k.split('.')[2])
linear_id = int(k.split('.')[3])
weight_or_bias = k.split('.')[-1]
new_k1 = 'module.transformer.decoder.bbox_embed.' + \
str(reg_layer_id) + '.layers.' + str(linear_id // 2) + '.' + weight_or_bias
new_k2 = 'module.bbox_embed.' + \
str(reg_layer_id) + '.layers.' + str(linear_id // 2) + '.' + weight_or_bias
new_ckpt[new_k1] = new_v # Add the key and value to the original checkpoint dict
new_ckpt[new_k2] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('bbox_head.cls_branches.6'):
# mmdet在contrastive_embed中添加了bias项
# 但是decoder应该是0~5，所以6应该是采取两阶段微调后对应的enc_out.class_embed
new_k = 'module.transformer.enc_out_class_embed.bias'
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('bbox_head.cls_branches'):
# mmdet在contrastive_embed中添加了bias项
new_k1 = 'module.transformer.decoder.class_embed.' + k[-6:]
new_k2 = 'module.class_embed.' + k[-6:]
new_ckpt[new_k1] = new_v # Add the key and value to the original checkpoint dict
new_ckpt[new_k2] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('memory_trans_'):
if k.startswith('memory_trans_fc'):
new_k = k.replace('memory_trans_fc', 'module.transformer.enc_output')
elif k.startswith('memory_trans_norm'):
new_k = k.replace('memory_trans_norm', 'module.transformer.enc_output_norm')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('encoder'):
new_k = k.replace('encoder', 'module.transformer.encoder')
new_k = new_k.replace('norms.0', 'norm1')
new_k = new_k.replace('norms.1', 'norm2')
new_k = new_k.replace('norms.2', 'norm3')
new_k = new_k.replace('ffn.layers.0.0', 'linear1')
new_k = new_k.replace('ffn.layers.1', 'linear2')
if 'text_layers' in new_k:
new_k = new_k.replace('self_attn.attn', 'self_attn')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('level_embed'):
new_k = k.replace('level_embed', 'module.transformer.level_embed')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('neck.convs'):
new_k = k.replace('neck.convs', 'module.input_proj')
new_k = new_k.replace('neck.extra_convs.0', 'neck.convs.3')
new_k = new_k.replace('conv.weight', '0.weight')
new_k = new_k.replace('conv.bias', '0.bias')
new_k = new_k.replace('gn.weight', '1.weight')
new_k = new_k.replace('gn.bias', '1.bias')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif 'neck.extra_convs.0' in k:
new_k = k.replace('neck.extra_convs.0', 'neck.convs.3')
new_k = new_k.replace('neck.convs', 'module.input_proj')
new_k = new_k.replace('conv.weight', '0.weight')
new_k = new_k.replace('conv.bias', '0.bias')
new_k = new_k.replace('gn.weight', '1.weight')
new_k = new_k.replace('gn.bias', '1.bias')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('text_feat_map'):
new_k = k.replace('text_feat_map', 'module.feat_map')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('language_model.language_backbone.body.model'):
new_k = k.replace('language_model.language_backbone.body.model', 'module.bert')
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
elif k.startswith('backbone'):
new_k = k.replace('backbone', 'module.backbone.0')
if 'patch_embed.projection' in new_k:
new_k = new_k.replace('patch_embed.projection', 'patch_embed.proj')
elif 'drop_after_pos' in new_k:
new_k = new_k.replace('drop_after_pos', 'pos_drop')
if 'stages' in new_k:
new_k = new_k.replace('stages', 'layers')
if 'ffn.layers.0.0' in new_k:
new_k = new_k.replace('ffn.layers.0.0', 'mlp.fc1')
elif 'ffn.layers.1' in new_k:
new_k = new_k.replace('ffn.layers.1', 'mlp.fc2')
elif 'attn.w_msa' in new_k:
new_k = new_k.replace('attn.w_msa', 'attn')
if 'downsample' in k:
if 'reduction.' in k:
new_v = correct_unfold_reduction_order(v)
elif 'norm.' in k:
new_v = correct_unfold_norm_order(v)
new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict
#########################################################################
else:
print('skip:', k)
continue
# if 'module.transformer.decoder.bbox_embed' in new_k:
# new_k = new_k.replace('module.transformer.decoder.bbox_embed', 'module.bbox_embed')
# if new_k.startswith('module'):
# new_k = new_k.replace('module.', '')
return new_ckpt
def main():
parser = argparse.ArgumentParser(
description='Convert keys to GroundingDINO style.')
parser.add_argument(
'src',
nargs='?',
default='grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth',
help='src model path or url')
# The dst path must be a full path of the new checkpoint.
parser.add_argument(
'dst',
nargs='?',
default='check_mmdet_to_groundingdino.pth',
help='save path')
args = parser.parse_args()
checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
# mmdet中是state_dict而不是model
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
weight = convert(state_dict)
torch.save(weight, args.dst)
# sha = subprocess.check_output(['sha256sum', args.dst]).decode()
# sha = calculate_sha256(args.dst)
# final_file = args.dst.replace('.pth', '') + '-{}.pth'.format(sha[:8])
# subprocess.Popen(['mv', args.dst, final_file])
print(f'Done!!, save to {args.dst}')
if __name__ == '__main__':
main()
# skip: dn_query_generator.label_embedding.weight |